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Artificial neural networks for NAA: proof of concept on data analysed with k0-based software

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Abstract

Artificial intelligence methods such as artificial neural networks, Bayesian networks, genetic algorithms, and others, have shown great potential for application, not only as classification schemes, but also in numerical data analysis. In this work, we explore how, from a limited number of spectra (around 200), an ANN could be efficiently developed, using data augmentation techniques and optimized architecture, and used to analyse neutron activation analysis (NAA) data. The IAEA Collaborating Centre Research Institute Delft (RID), Netherlands, has collected NAA data sets consisting of one single spectrum per sample to determine one single element (selenium), with addition of a marker (caesium) for flux normalization, all irradiated and measured the exact same way and analysed with k0-based software. The problem studied is one of the simplest that can be addressed with NAA; therefore the present work is intended merely as proof of concept that ANNs can perform well in NAA data analysis of simple problems. We present the results and discuss how to extend the present work to more demanding problems in NAA.

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References

  1. International Atomic Energy Agency (2022) Research reactor database. https://nucleus.iaea.org/rrdb/#/home

  2. International Atomic Energy Agency (2018) Proficiency testing by interlaboratory comparison performed in 2010–2015 for neutron activation analysis and other analytical techniques. IAEA-TECDOC-1831, IAEA, Vienna

  3. International Atomic Energy Agency (2018) Development of an integrated approach to routine automation of neutron activation analysis. IAEA-TECDOC-1839, IAEA, Vienna

  4. International Atomic Energy Agency (2018) Advances in neutron activation analysis of large objects with emphasis on archaeological examples results of a coordinated research project. IAEA-TECDOC-1838, IAEA, Vienna

  5. International Atomic Energy Agency (2022) Quality assurance and quality control in neutron activation analysis: a guide to practical approaches. IAEA technical reports series 487, IAEA, Vienna

  6. Simonits A, Corte F, Hoste J (1975) Single-comparator methods in reactor neutron activation analysis. J Radioanal Chem 24:31–46. https://doi.org/10.1007/BF02514380

    Article  CAS  Google Scholar 

  7. Pessoa Barradas N, Vieira A (2000) Artificial neural network algorithm for analysis of rutherford backscattering data. Phys Rev E 62:5818–5829. https://doi.org/10.1103/PhysRevE.62.5818

    Article  Google Scholar 

  8. Vieira A, Pessoa Barradas N (2001) Composition of NiTaC films on Si using neural networks analysis of elastic backscattering data. Nucl Instrum Method Phys Res B 174:367–372. https://doi.org/10.1016/S0168-583X(00)00621-2

    Article  CAS  Google Scholar 

  9. Nené NR, Vieira A, Pessoa Barradas N (2006) Artificial neural network analysis of RBS and ERDA spectra of multilayered multielemental samples. Nucl Instrum Method Phys Res B 246:471–478. https://doi.org/10.1016/j.nimb.2006.01.016

    Article  CAS  Google Scholar 

  10. Pessoa Barradas N, Vieira A, Patrício R (2002) Artificial neural networks for automation of rutherford backscattering spectroscopy experiments and data analysis. Phys Rev E 65:066703. https://doi.org/10.1103/PhysRevE.65.066703

    Article  CAS  Google Scholar 

  11. Li F, Gu Z, Ge L et al (2019) Application of artificial neural networks to X-ray fluorescence spectrum analysis. X-Ray Spectrom 48:138–150. https://doi.org/10.1002/xrs.2996

    Article  CAS  Google Scholar 

  12. Yoshida E, Shizuma K, Endo S, Oka T (2002) Application of neural networks for the analysis of gamma-ray spectra measured with a Ge spectrometer. Nucl Instrum Method Phys Res A 484:557–563. https://doi.org/10.1016/S0168-9002(01)01962-3

    Article  CAS  Google Scholar 

  13. Roshani GH, Eftekhari-Zadeh E, Shama F, Salehizadeh A (2017) Combined application of neutron activation analysis using IECF device and neural network for prediction of cement elements. Radiat Detect Technol Method 1:23. https://doi.org/10.1007/s41605-017-0025-z

    Article  Google Scholar 

  14. Medhat ME (2015) Artificial neural network: a tool for rapid quantitative elemental analysis using neutron activation analysis. Int J Adv Res Electr Electron Instrum Eng 4:5497–5501

    Google Scholar 

  15. Lee D (2019) Application of artificial neural network to prompt gamma neutron activation analysis for chemical warfare agents identification. https://doi.org/10.2172/1565918

  16. Bilton KJ, Joshi THY, Bandstra MS et al (2021) Neural network approaches for mobile spectroscopic gamma-ray source detection. JNE 2:190–206. https://doi.org/10.3390/jne2020018

    Article  Google Scholar 

  17. Hossny K, Hossny AH, Magdi S et al (2020) Detecting shielded explosives by coupling prompt gamma neutron activation analysis and deep neural networks. Sci Rep 10:13467. https://doi.org/10.1038/s41598-020-70537-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Braga CC, Dias MS (2002) Application of neural networks for unfolding neutron spectra measured by means of bonner spheres. Nucl Instrum Method Phys Res, Sect A 476:252–255. https://doi.org/10.1016/S0168-9002(01)01464-4

    Article  CAS  Google Scholar 

  19. Bode P, Korthoven PJM, de Bruin M (1987) Microprocessor-controlled facility for INAA using short half-life nuclides. J Radioanal Nucl Chem 113:371–378. https://doi.org/10.1007/BF02050509

    Article  CAS  Google Scholar 

  20. Blaauw M (1999) The reference peak areas of the 1995 IAEA test spectra for gamma-ray spectrum analysis programs are absolute and traceable. Nucl Instrum Method Phys Res A 432:74–76. https://doi.org/10.1016/S0168-9002(99)00257-0

    Article  CAS  Google Scholar 

  21. Blaauw M (1994) The holistic analysis of gamma-ray spectra in instrumental neutron activation analysis. Nucl Instrum Meth A 353:269–271

    Article  CAS  Google Scholar 

  22. Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. In: Guyon I, Luxburg UV, Bengio S et al (eds) Advances in neural information processing systems. Curran Associates Inc, New York

    Google Scholar 

  23. Yang Z, Dai Z, Yang Y et al (2019) XLNet: generalized autoregressive pretraining for language understanding. In: Wallach H, Larochelle H, Beygelzimer A et al (eds) Advances in neural information processing systems. Curran Associates Inc, New York

    Google Scholar 

  24. Touvron H, Cord M, Douze M et al. (2021) Training data-efficient image transformers & distillation through attention. In: Proceedings of the 38th international conference on machine learning. PMLR, pp 10347–10357

  25. Chollet F (2021) Deep learning with python, 2nd edn. Manning Publications, Shelter Island

    Google Scholar 

  26. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. The MIT Press, Cambridge, Massachusetts

    Google Scholar 

  27. Liu H, Dai Z, So DR, Le QV (2021) Pay attention to MLPs. Adv Neural Inf Process Syst 34:9204–9215. https://doi.org/10.48550/ARXIV.2105.08050

    Article  Google Scholar 

  28. Tang C, Zhao Y, Wang G et al. (2021) Sparse MLP for image recognition: is self-attention really necessary. https://doi.org/10.48550/ARXIV.2109.05422

  29. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958

    Google Scholar 

  30. Hendrycks D, Gimpel K (2016) Gaussian error linear units (GELUs). https://doi.org/10.48550/ARXIV.1606.08415

  31. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. https://doi.org/10.48550/ARXIV.1412.6980

  32. Abadi M, Agarwal A, Barham P et al. (2015) Tensorflow: large-scale machine learning on heterogeneous systems

  33. Pinho HFR, Vieira A, Nené NR, Barradas NP (2005) Artificial neural network analysis of multiple IBA spectra. Nucl Instrum Method Phys Res B 228:383–387. https://doi.org/10.1016/j.nimb.2004.10.075

    Article  CAS  Google Scholar 

  34. Gui J, Sun Z, Wen Y, Tao D, Ye J (2021) A review on generative adversarial networks: algorithms, theory, and applications. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2021.3130191

    Article  Google Scholar 

  35. Lebese T, Ruan X (2022) The use of generative adversarial networks to characterise new physics in multi-lepton final states at the LHC. https://arxiv.org/pdf/2105.14933.pdf

  36. IAEA-Physics-neutrons (2022) NAA-ANN-1: proof of concept of application of artificial neural networks to neutron activation analysis data. https://github.com/IAEA-Physics-neutrons/NAA-ANN-1

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Authors and Affiliations

  1. International Atomic Energy Agency, Vienna, Austria

    N. Pessoa Barradas & N. Farjallah

  2. Hazy, London, UK

    A. Vieira

  3. Reactor Institute Delft, Delft University of Technology, Delft, Netherlands

    M. Blaauw

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  1. N. Pessoa Barradas
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  2. N. Farjallah
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  3. A. Vieira
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  4. M. Blaauw
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Correspondence to N. Pessoa Barradas.

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Barradas, N.P., Farjallah, N., Vieira, A. et al. Artificial neural networks for NAA: proof of concept on data analysed with k0-based software. J Radioanal Nucl Chem 332, 3421–3429 (2023). https://doi.org/10.1007/s10967-022-08568-8

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